%matplotlib inline

# OPTIONAL: Load the "autoreload" extension so that code can change
%load_ext autoreload

# OPTIONAL: always reload modules so that as you change code in src, it gets loaded
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Geometric Model

Please enter a brief description of the model, considering that more detailed information will be entered in the following sections

General data of the building

import pandas as pd

input_parameters = pd.read_csv('../data/processed/cultural-e-input.csv')

Please enter the following general information related to the building

Field	Value
Type of building	-
Location	-
Number of thermal zones	-
Photovoltaic system	-
Technology installed	-
Position [façade or roof]	-
Azimuth [°]	-
Space for additional information/system	-

Quantity	ID
Gross floor area [m2]	IN_GFA
Net floor area [m2]	IN_NIA
S/V ratio	IN_SV
PV capacity [kWp]	IN_PV_kWp
PV area [m2]	IN_A_PV
Battery capacity [kWh]	IN_PV_bat
Tilt angle [°]	IN_PV_Tilt

input_parameters[[
        'IN_GFA', 'IN_NIA', 'IN_SV', 'IN_A_PV', 'IN_PV_bat',
        'IN_PV_Tilt'
    ]]

	IN_GFA	IN_NIA	IN_SV	IN_A_PV	IN_PV_bat	IN_PV_Tilt
0	3.0	2.0	4.0	3.0	4.0	3.0

Thermal zone

Please fill in this table for the information related to the thermal zones. This information must be provided for each thermal zone.

Please fill in this table with the general information required on the internal gains.

Figures

Please consider adding the heating setpoint schedule chart.
Please consider adding the cooling setpoint schedule chart.
Please consider adding the occupancy schedule chart.
Please consider adding the lighting schedule chart.
Please consider adding the appliances schedule chart.

Using the following sintax: ![placeholder](./placeholder.jpg)

Building envelope

Opaque envelope components

Please fill in the following table with the information related to the opaque envelope components. Please enter manually the entire table except for the section “U-value [W/m²K]”

Results

from pvlib.iotools import read_epw
weather, meta = read_epw('../data/processed/meteo.epw')
weather.head()

	year	month	day	hour	minute	data_source_unct	temp_air	temp_dew	relative_humidity	atmospheric_pressure	...	ceiling_height	present_weather_observation	present_weather_codes	precipitable_water	aerosol_optical_depth	snow_depth	days_since_last_snowfall	albedo	liquid_precipitation_depth	liquid_precipitation_quantity
2005-01-01 00:00:00+01:00	2005	1	1	1	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	7.1	1.5	68	98414	...	99999	9	999999999	10	0.118	18	0	0.732	0.0	99.0
2005-01-01 01:00:00+01:00	2005	1	1	2	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.7	1.5	69	98414	...	99999	9	999999999	10	0.118	18	0	0.717	0.0	99.0
2005-01-01 02:00:00+01:00	2005	1	1	3	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.4	1.0	68	98414	...	99999	9	999999999	10	0.118	18	0	0.702	0.0	99.0
2005-01-01 03:00:00+01:00	2005	1	1	4	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.2	0.8	68	98414	...	99999	9	999999999	10	0.118	18	0	0.689	0.0	99.0
2005-01-01 04:00:00+01:00	2005	1	1	5	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.0	0.5	68	98414	...	99999	9	999999999	10	0.118	18	0	0.676	0.0	99.0

5 rows × 35 columns

summary = pd.read_csv('../data/processed/summary.csv')
summary.head()

	Zonenr	Rel_BAL	BAL_ENERGY	DQAIRdt	QHEAT	QCOOL	QINF	QVENT	QCOUPL	QTRANS	QGAININT	QWGAIN	QSOLGAIN	QSOLAIR
0	1	0.0	1.373000e-08	234.9	7298.0	9646.0	0.0	0.0	-431700.0	434200.0	0.0	0.0	0.0	0.0
1	2	0.0	7.868000e-09	113.0	29990000.0	165400.0	-37780000.0	0.0	7253000.0	-238700.0	0.0	0.0	939900.0	0.0
2	3	0.0	-1.623000e-09	1212.0	58950.0	255000.0	-1105000.0	0.0	-998100.0	-5418000.0	1215000.0	0.0	6503000.0	0.0
3	4	0.0	-4.290000e-08	2167.0	20140.0	1996000.0	-1209000.0	0.0	-4627000.0	-9161000.0	4704000.0	0.0	12270000.0	0.0
4	5	0.0	3.032000e-09	1822.0	47720.0	1057000.0	-747700.0	0.0	-1713000.0	-3799000.0	2227000.0	0.0	5044000.0	0.0

energy_zones = pd.read_csv('../data/processed/energy_zones.csv')
energy_zones.head()

	TIME	REL_BAL_ENERGY	1_B4_QBAL	1_B4_DQAIRdT	1_B4_QHEAT	1_B4_QCOOL	1_B4_QINF	1_B4_QVENT	1B4_QCOUP	1_B4_QTRANS	...	5_B4_QHEAT	5_B4_QCOOL	5_B4_QINF	5_B4_QVENT	5B4_QCOUP	5_B4_QTRANS	5_B4_QGINT	5_B4_QWGAIN	5_B4_QSOL	5_B4_QSOLAIR
0	0	0.0	1.731000e-11	-8.356	0.0	0.0	0.0	0.0	-41.42	33.07	...	0.0	0.0	-83.14	0.0	62.85	-554.3	374.9	0.0	6.750000e-12	0.0
1	1	0.0	-4.974000e-13	-2.190	0.0	0.0	0.0	0.0	-42.78	40.59	...	0.0	0.0	-56.99	0.0	97.38	-387.7	374.9	0.0	-1.031000e-12	0.0
2	2	0.0	9.365000e-12	2.555	0.0	0.0	0.0	0.0	-27.60	30.15	...	0.0	0.0	-36.10	0.0	82.31	-395.9	374.9	0.0	5.851000e-12	0.0
3	3	0.0	4.469000e-12	-4.190	0.0	0.0	0.0	0.0	-29.01	24.82	...	0.0	0.0	-37.67	0.0	72.04	-414.2	374.9	0.0	-7.967000e-13	0.0
4	4	0.0	3.180000e-12	-1.234	0.0	0.0	0.0	0.0	-27.87	26.64	...	0.0	0.0	-38.77	0.0	64.68	-407.8	374.9	0.0	-7.522000e-12	0.0

5 rows × 62 columns

cultural_e = pd.read_csv('../data/processed/cultural-e.csv')
cultural_e.head()

	TIME	SQHEAT_1	SQCOOL_1	TAIR_F1undfA1	TOP_F1undfA1	TAIR_F1undfA2	TOP_F1undfA2	TAIR_F1nightA1	TOP_F1nightA1	TAIR_F1dayA	...	label.50	label.51	label.52	label.53	label.54	label.55	label.56	label.57	label.58	label.59
0	0	9360.0	0.0	21.9	21.9	21.0	20.8	21.9	21.9	22.9	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1	9440.0	0.0	21.8	21.9	21.0	20.8	21.9	21.8	22.6	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	2	6740.0	0.0	21.8	21.8	21.0	20.8	21.8	21.7	22.6	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	3	4460.0	0.0	21.8	21.8	21.0	20.8	21.8	21.7	22.6	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	4	4640.0	0.0	21.7	21.8	21.0	20.8	21.8	21.7	22.6	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 101 columns

from src.visualization import visualize as viz

Climate

The weather conditions of a location play an important role in the energy performance of a building. In the next subsections, some results in terms of outdoor air temperature, global horizontal irradiance, and relative humidity, are presented.

viz.air_temperature(weather)

-	-
Description of the graph	Hourly dry-bulb temperature distribution and the cumulative frequency of a standard year.
Interpretation of results	[Please enter manually this field]

viz.relative_humidity(weather)

viz.horizontal_irradiance(weather)

Energy

This section shows the main results in terms of energy balance of the building, energy consumption considering the total energy use of the house and overall heating load considering an ideal heating and/or cooling system. It also gives information on the use of renewable energy in case a photovoltaic system has been installed in the building.

viz.heating_loads(cultural_e)

viz.cooling_loads(cultural_e)

viz.energy_balance(summary)

for zone in ['1', '2', '3', '4', '5']:
    viz.zone_energy_balance(energy_zones, zone)

viz.monthly_consumption(cultural_e)

viz.self_production_consumption(cultural_e)

Comfort

This section has been organized to show the main results in terms of thermal comfort, visual comfort and IAQ. For each group of output, you will be asked to enter the results for each thermal zone. If you think it is useful to assess the comfort in only some areas of the building or only in one, enter the results only for those useful for your evaluation.

Mean indoor temperature

viz.airt_heatmap(cultural_e, 'F1dayA')

viz.psychrochart(cultural_e.sample(n=1000), 'F1dayA', weather.sample(n=1000)).get_figure()

Indoor Air Quality

In order to assess the air quality of the building during the occupied time, considering the people as one of the main pollution sources, the level of the CO2 concentration generated by the occupants, need to be calculated. The limits for indoor CO2 concentrations leading to the four IAQ categories have been calculated in accordance with the standard EN 16798-1: 2019.

Carbon Dioxide

viz.iaq_co2(cultural_e, ['F1dayA'], ['F1nightA1', 'F1nightA2'])

Relative Humidity

Another necessary parameter to evaluate the internal comfort of a building is the indoor relative humidity. This is important because high or low percentages lead to humid or dry environment, respectively, which has a direct effect on human well-being. An effective way to evaluate this data is to classify the number of hours in which the relative humidity of a thermal zone falls within the categories for humidification and dehumidification, identified in standard EN 16798-1: 2019.

viz.relh(cultural_e, ['RELHUM_F1dayA', 'RELHUM_F1nightA1', 'RELHUM_F1nightA2'], ['OCC_F1dayA', 'OCC_F1nightA1', 'OCC_F1nightA2'])

Natural Ventilation

The frequency of opening the windows can be considered an interesting indicator in the evaluation of some aspects related to the performance of the building. thanks to this result it is possible to evaluate, for example, whether the action of natural ventilation alone can guarantee an acceptable level of internal comfort, whether it affects the energy consumption of the building as well as giving indications on how the occupants interact with the building.

viz.win_heatmap(cultural_e, 'F1dayA')

Activation of the shadings

viz.shd_heatmap(cultural_e, 'F1dayA')